EVOLUTION-MANAGER

Edit File: scan_on_cpu.hpp

//---------------------------------------------------------------------------// // Copyright (c) 2016 Jakub Szuppe <j.szuppe@gmail.com> // // Distributed under the Boost Software License, Version 1.0 // See accompanying file LICENSE_1_0.txt or copy at // http://www.boost.org/LICENSE_1_0.txt // // See http://boostorg.github.com/compute for more information. //---------------------------------------------------------------------------// #ifndef BOOST_COMPUTE_ALGORITHM_DETAIL_SCAN_ON_CPU_HPP #define BOOST_COMPUTE_ALGORITHM_DETAIL_SCAN_ON_CPU_HPP #include <iterator> #include <boost/compute/device.hpp> #include <boost/compute/kernel.hpp> #include <boost/compute/command_queue.hpp> #include <boost/compute/algorithm/detail/serial_scan.hpp> #include <boost/compute/detail/meta_kernel.hpp> #include <boost/compute/detail/iterator_range_size.hpp> #include <boost/compute/detail/parameter_cache.hpp> namespace boost { namespace compute { namespace detail { template<class InputIterator, class OutputIterator, class T, class BinaryOperator> inline OutputIterator scan_on_cpu(InputIterator first, InputIterator last, OutputIterator result, bool exclusive, T init, BinaryOperator op, command_queue &queue) { typedef typename std::iterator_traits<InputIterator>::value_type input_type; typedef typename std::iterator_traits<OutputIterator>::value_type output_type; const context &context = queue.get_context(); const device &device = queue.get_device(); const size_t compute_units = queue.get_device().compute_units(); boost::shared_ptr<parameter_cache> parameters = detail::parameter_cache::get_global_cache(device); std::string cache_key = "__boost_scan_cpu_" + boost::lexical_cast<std::string>(sizeof(T)); // for inputs smaller than serial_scan_threshold // serial_scan algorithm is used uint_ serial_scan_threshold = parameters->get(cache_key, "serial_scan_threshold", 16384 * sizeof(T)); serial_scan_threshold = (std::max)(serial_scan_threshold, uint_(compute_units)); size_t count = detail::iterator_range_size(first, last); if(count == 0){ return result; } else if(count < serial_scan_threshold) { return serial_scan(first, last, result, exclusive, init, op, queue); } buffer block_partial_sums(context, sizeof(output_type) * compute_units ); // create scan kernel meta_kernel k("scan_on_cpu_block_scan"); // Arguments size_t count_arg = k.add_arg<uint_>("count"); size_t init_arg = k.add_arg<output_type>("initial_value"); size_t block_partial_sums_arg = k.add_arg<output_type *>(memory_object::global_memory, "block_partial_sums"); k << "uint block = (count + get_global_size(0))/(get_global_size(0) + 1);\n" << "uint index = get_global_id(0) * block;\n" << "uint end = min(count, index + block);\n" << "if(index >= end) return;\n"; if(!exclusive){ k << k.decl<output_type>("sum") << " = " << first[k.var<uint_>("index")] << ";\n" << result[k.var<uint_>("index")] << " = sum;\n" << "index++;\n"; } else { k << k.decl<output_type>("sum") << ";\n" << "if(index == 0){\n" << "sum = initial_value;\n" << "}\n" << "else {\n" << "sum = " << first[k.var<uint_>("index")] << ";\n" << "index++;\n" << "}\n"; } k << "while(index < end){\n" << // load next value k.decl<const input_type>("value") << " = " << first[k.var<uint_>("index")] << ";\n"; if(exclusive){ k << "if(get_global_id(0) == 0){\n" << result[k.var<uint_>("index")] << " = sum;\n" << "}\n"; } k << "sum = " << op(k.var<output_type>("sum"), k.var<output_type>("value")) << ";\n"; if(!exclusive){ k << "if(get_global_id(0) == 0){\n" << result[k.var<uint_>("index")] << " = sum;\n" << "}\n"; } k << "index++;\n" << "}\n" << // end while "block_partial_sums[get_global_id(0)] = sum;\n"; // compile scan kernel kernel block_scan_kernel = k.compile(context); // setup kernel arguments block_scan_kernel.set_arg(count_arg, static_cast<uint_>(count)); block_scan_kernel.set_arg(init_arg, static_cast<output_type>(init)); block_scan_kernel.set_arg(block_partial_sums_arg, block_partial_sums); // execute the kernel size_t global_work_size = compute_units; queue.enqueue_1d_range_kernel(block_scan_kernel, 0, global_work_size, 0); // scan is done if(compute_units < 2) { return result + count; } // final scan kernel meta_kernel l("scan_on_cpu_final_scan"); // Arguments count_arg = l.add_arg<uint_>("count"); block_partial_sums_arg = l.add_arg<output_type *>(memory_object::global_memory, "block_partial_sums"); l << "uint block = (count + get_global_size(0))/(get_global_size(0) + 1);\n" << "uint index = block + get_global_id(0) * block;\n" << "uint end = min(count, index + block);\n" << k.decl<output_type>("sum") << " = block_partial_sums[0];\n" << "for(uint i = 0; i < get_global_id(0); i++) {\n" << "sum = " << op(k.var<output_type>("sum"), k.var<output_type>("block_partial_sums[i + 1]")) << ";\n" << "}\n" << "while(index < end){\n"; if(exclusive){ l << l.decl<output_type>("value") << " = " << first[k.var<uint_>("index")] << ";\n" << result[k.var<uint_>("index")] << " = sum;\n" << "sum = " << op(k.var<output_type>("sum"), k.var<output_type>("value")) << ";\n"; } else { l << "sum = " << op(k.var<output_type>("sum"), first[k.var<uint_>("index")]) << ";\n" << result[k.var<uint_>("index")] << " = sum;\n"; } l << "index++;\n" << "}\n"; // compile scan kernel kernel final_scan_kernel = l.compile(context); // setup kernel arguments final_scan_kernel.set_arg(count_arg, static_cast<uint_>(count)); final_scan_kernel.set_arg(block_partial_sums_arg, block_partial_sums); // execute the kernel global_work_size = compute_units; queue.enqueue_1d_range_kernel(final_scan_kernel, 0, global_work_size, 0); // return iterator pointing to the end of the result range return result + count; } } // end detail namespace } // end compute namespace } // end boost namespace #endif // BOOST_COMPUTE_ALGORITHM_DETAIL_SCAN_ON_CPU_HPP